"Zero-ETL RAG: Simplifying Cloud Architectures with Amazon S3 Vectors" by Ashtikar Ayush
#generative-ai #rag #serverless #python #amazon-bedrock

"Zero-ETL RAG: Simplifying Cloud Architectures with Amazon S3 Vectors" by Ashtikar Ayush
#generative-ai #rag #serverless #python #amazon-bedrock
With GenePattern Copilot we found that a non-augmented model initially beat our RAG assistant.
The failure was naive chunking splitting Q&A pairs. The fix was preprocessing with LLM fact extraction, ensuring every vector DB row was an atomic statement.
Read the blog post below.
#RAG #AgenticAI
"RAG from Basics to Advanced (Part 8): Building a RAG System Locally" by Pawan Sharma
🤖 How are companies using Generative AI in 2026?
From customer service and coding to data analysis, RAG, AI agents, and workflow automation, GenAI is becoming a powerful enterprise technology.
Read: www.linkedin.com/pulse/sap-ra...
📞 +91-7032290546
Don’t default to a vector database for every LLM feature. Use plain prompting for small, stable context, keyword search for exact terms or IDs, and hybrid search when production RAG needs lexical precision plus semantic recall. #RAG
"Production-Ready RAG Application with Amazon Bedrock" by Abhishek Kumar
#amazon-bedrock #generative-ai #rag #artificial-intelligence
Local AI Use Case - Isolated RAG system with NFC asset lookup
🔎 Die Information ist da – aber RAG findet sie nicht.
Wolfgang Niebuhr zeigt auf der #jaxcon, wo RAG-Systeme in der Praxis scheitern: Chunking, Embeddings, Retrieval & Halluzinationen.
📍München | 📅 03.11. | ⌚ 10:45 Uhr
🔗https://f.mtr.cool/yrglu7sipw
"RAG from Basics to Advanced (Part 7): Production Challenges Latency, Caching, Cost, Monitoring" by Pawan Sharma
#rag #ai #agents #machine-learning #production
"Why Production RAG Pipelines Need More Than a Vector Database on AWS" by Pasindu Lanka
"Building Production-Ready Generative AI Applications on AWS: From RAG and AI Agents to Secure, Scalable Cloud Deployment" by Viraj Krishnaraj
#generative-ai #amazon-bedrock #ai-agents #rag #cloud-security
Built five RAG architectures on the same Azure setup, with the same 15 questions, so I could actually compare them.
Best surprise: Corrective RAG did better on the 12 questions it wasn't built for than on the 3 it was built for.
Design, assemble, and tune a retrieval-augmented generation system on a standard laptop without cloud infrastructure. Includes key steps and lightweight packages needed for this offline system. 🔍💻 #RAG #TechNews #Innovation
Day 64/100 of #100DaysWithAI 📚
Length-based chunking throws away the contents page. Make retrieval generate a real ToC section and it can't name one that isn't there.
🔗 https://github.com/aurimas13/100-Days-With-AI
🤝 Claude Code-assisted, reviewed by me.